Artificial Neural Networks and Machine Learning - ICANN 2020 : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part I / edited by Igor Farkaš, Paolo Masulli, Stefan Wermter.

Format:

Book

Contributor:

Farkaš, Igor., Editor.

Masulli, Paolo, Editor.

Wermter, Stefan, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Theoretical computer science and general issues 2512-2029 ; SL 1, 12396

Theoretical Computer Science and General Issues, 2512-2029 ; 12396

Language:

English

Subjects (All):

Artificial intelligence.

Computer networks.

Image processing-Digital techniques.

Computer vision.

Computers.

Application software.

Computer engineering.

Artificial Intelligence.

Computer Communication Networks.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computing Milieux.

Computer and Information Systems Applications.

Computer Engineering and Networks.

Local Subjects:

Artificial Intelligence.

Computer Communication Networks.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computing Milieux.

Computer and Information Systems Applications.

Computer Engineering and Networks.

Physical Description:

1 online resource (XXVII, 891 pages) : 348 illustrations, 260 illustrations in color.

Edition:

1st ed. 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.* The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic.

Contents:

Adversarial Machine Learning

On the security relevance of initial weights in deep neural networks

Fractal Residual Network for Face Image Super-Resolution

From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders

Generating Adversarial Texts for Recurrent Neural Networks

Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation

Computational Analysis of Robustness in Neural Network Classifiers

Bioinformatics and Biosignal Analysis

Convolutional neural networks with reusable full-dimension-long layers for feature selection and classification of motor imagery in EEG signals

Compressing Genomic Sequences by Using Deep Learning

Learning Tn5 sequence bias from ATAC-seq on naked chromatin

Tucker tensor decomposition of multi-session EEG data

Reactive Hand Movements from Arm Kinematics and EMG Signals Based on Hierarchical Gaussian Process Dynamical Models

Cognitive Models

Investigating Efficient Learning and Compositionality in Generative LSTM Networks

Fostering Event Compression using Gated Surprise

Physiologically-inspired Neural Circuits for the Recognition of Dynamic Faces

Hierarchical Modeling with Neurodynamical Agglomerative Analysis

Convolutional Neural Networks and Kernel Methods

Deep and Wide Neural Networks Covariance Estimation

Monotone deep Spectrum Kernels

Permutation Learning in Convolutional Neural Networks for Time Series Analysis

Deep Learning Applications I

GTFNet: Ground Truth Fitting Network for Crowd Counting

Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography

Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision

Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-Learning

Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders

Deep Learning Applications II

Novel Sketch-based 3D Model Retrieval via Cross-domain Feature Clustering and Matching

Multi-objective Cuckoo Algorithm for Mobile Devices Network Architecture Search

DeepED: a Deep Learning Framework for Estimating Evolutionary Distances

Interpretable Machine Learning Structure for an Early Prediction of Lane Changes

Explainable Methods

Convex Density Constraints for Computing Plausible Counterfactual Explanations

Identifying Critical States by the Action-Based Variance of Expected Return

Explaining Concept Drift by Means of Direction

Few-shot Learning

Context Adaptive Metric Model for Meta-Learning

Ensemble-Based Deep Metric Learning for Few-Shot Learning

More Attentional Local Descriptors for Few-shot Learning

Implementation of Siamese-based Few-shot Learning Algorithms for the Distinction of COPD and Asthma Subjects

Few-Shot Learning for Medical Image Classification

Generative Adversarial Network

Adversarial Defense via Attention-based Randomized Smoothing

Learning to Learn from Mistakes: Robust Optimization for Adversarial Noise

Unsupervised Anomaly Detection with a GAN Augmented Autoencoder

An Efficient Blurring-Reconstruction Model to Defend against Adversarial Attacks

EdgeAugment: Data Augmentation by Fusing and Filling Edge Map

Face Anti-spoofing with a Noise-Attention Network Using Color-Channel Difference Images

Generative and Graph Models

Variational Autoencoder with Global- and Medium Timescale Auxiliaries for Emotion Recognition from Speech

Improved Classification Based on Deep Belief Networks

Temporal Anomaly Detection by Deep Generative Models with Applications to Biological Data

Inferring, Predicting, and Denoising Causal Wave Dynamics

PART-GAN: Privacy-Preserving Time-Series Sharing

EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs

Hybrid Neural-symbolic Architectures

Facial Expression Recognition Method based on a Part-based Temporal Convolutional Network with a Graph-Structured Representation

Generating Facial Expressions Associated with Text

Image Processing

Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models

Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases

Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE)

SOM-based System for Sequence Chunking and Planning

Bilinear Models for Machine Learning

Enriched Feature Representation and Combination for Deep Saliency Detection

Spectral Graph Reasoning Network for Hyperspectral Image Classification

Salient Object Detection with Edge Recalibration

Multi-Scale Cross-Modal Spatial Attention Fusion for Multi-label Image Recognition

A New Efficient Finger-Vein Verification Based on Lightweight Neural Network Using Multiple Schemes

Medical Image Processing

SU-Net: An Efficient Encoder-Decoder Model of Federated Learning for Brain Tumor Segmentation

Synthesis of Registered Multimodal Medical Images with Lesions

ACE-Net: Adaptive Context Extraction Network for Medical Image Segmentation

Wavelet U-Net for Medical Image Segmentation

Recurrent Neural Networks

Character-based LSTM-CRF with semantic features for Chinese Event Element Recognition

Sequence Prediction using Spectral RNNs

Attention Based Mechanism for Energy Load Time Series Forecasting: AN-LSTM

DartsReNet: Exploring new RNN cells in ReNet architectures

On Multi-modal Fusion for Freehand Gesture Recognition

Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data.

Other Format:

Printed edition:

ISBN:

978-3-030-61609-0

9783030616090

Access Restriction:

Restricted for use by site license.